Diverse Beam Search

Decoding Diverse Solutions from Neural Sequence Models

Beam search, the standard work-horse for decoding outputs from neural sequence models like RNNs produces generic and uninteresting sequences. This is inadequate for AI tasks with inherent ambiguity — for example, there can be multiple correct ways of describing the contents of an image. To overcome this we propose a diversity-promoting replacement, Diverse Beam Search that produces sequences that are significantly different — with runtime and memory requirements comparable to beam search.